
#Uploading libraries
import pandas as pd
import numpy as np
import os
import gensim #Topic Modelling Technicque called LDA (Latent Dirichlet Allocation)
from gensim.utils import simple_preprocess

from google.colab import files

uploaded=files.upload()

Saving all_kindle_review .csv to all_kindle_review .csv


#Reading the data
df1 = pd.read_csv('all_kindle_review .csv')
df1


#Separating columns
df=df1[['reviewText','summary']]


#Cleaning the data
import re
from bs4 import BeautifulSoup
from nltk.tokenize import WordPunctTokenizer
tok = WordPunctTokenizer()
pat1 = r'@[A-Za-z0-9]+'
pat2 = r'https?://[A-Za-z0-9./]+'
#pat3 = r'(\\x(.){2})'
#pat4 = r'b'
combined_pat = r'|'.join((pat1,pat2))
def review_cleaner(text):
    soup = BeautifulSoup(text, 'lxml')
    souped = soup.get_text()
    stripped = re.sub(combined_pat, '', souped)
    try:
        clean = stripped.decode("utf-8-sig").replace(u"\ufffd", "?")
    except:
        clean = stripped
    letters_only = re.sub("[^a-zA-Z]", " ", clean)
    lower_case = letters_only.lower()
    # During the letters_only process two lines above, it has created unnecessay white spaces,
    # I will tokenize and join together to remove unneccessary white spaces
    words = tok.tokenize(lower_case)
    return (" ".join(words)).strip()
testing = df['reviewText']
test_result = []
for t in testing:
    test_result.append(review_cleaner(t))
test_result[0]

'jace rankin may be short but he s nothing to mess with as the man who was just hauled out of the saloon by the undertaker knows now he s a famous bounty hunter in oregon in the s who when he shot the man in the saloon just finished a years long quest to avenge his sister s murder and is now trying to figure out what to do next when the snotty nosed farm boy he just rescued from a gang of bullies offers him money to kill a man who forced him off his ranch he reluctantly agrees to bring the man to justice but not to kill him outright but first he needs to tell his sister s widower the news kyla kyle springer bailey has been riding the trails and sleeping on the ground for the past month while trying to find jace she wants revenge on the man who killed her husband and took her ranch amongst other crimes and she s not so keen on the detour jace wants to take but she realizes she s out of options so she hides behind her boy persona as best she can and tries to keep pace when a confrontation along the way gets her shot and jace discovers that kyle s a kyla she has to come clean about the whole reason she needs this scoundrel dead and hope he ll still help her the book has its share of touching moments and slow blooming romance kyla we find out has good reason to fear men and hide behind a boy s persona watching jace slowly pull her out of that shell and help her conquer her fears was endearing her pain was real and deeply rooted and didn t just disappear in the face of his sexiness neither did his understandable aversion to marriage magically disappear after a round of nookie why would a man who s drifted from town to town for his entire adult life a man who s killed his fair share of men along the way feel he d make a good husband or father they re both the walking wounded and in a unique position to help each other they just need time to realize it however while it was packed to bursting with my favorite themes old west setting a heroine passing as male a morally ambiguous hero wounded souls road romance the kitchen sink there was a certain distance in the writing that kept me from getting carried away both characters were distinct and fully formed and i couldn t point to any glaring instances of bland telling or anything i just never felt really invested i enjoyed it quite a bit as it was an exciting trip but i was watching them from far away i definitely wasn t on the run alongside them add on a hasty and too tidy ending and the book left me feeling a bit less than completely satisfied i enjoyed the book but i can t say it was above average i d still read another book by the author note i read the re released ebook version and noticed there were a lot of formatting errors and missing words i had to mentally edit as i read for some sentences to make sense it wasn t too too bad but it was a little distracting'


!pip install vaderSentiment

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting vaderSentiment
  Downloading vaderSentiment-3.3.2-py2.py3-none-any.whl (125 kB)
     |████████████████████████████████| 125 kB 25.5 MB/s 
Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from vaderSentiment) (2.23.0)
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Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (3.0.4)
Installing collected packages: vaderSentiment
Successfully installed vaderSentiment-3.3.2


from nltk.sentiment.vader import SentimentIntensityAnalyzer


import nltk
nltk.download('vader_lexicon')

[nltk_data] Downloading package vader_lexicon to /root/nltk_data...

True


#Sentiment analysis
analyzer = SentimentIntensityAnalyzer()

df['compound'] = [analyzer.polarity_scores(x)['compound'] for x in df['reviewText']]
df['neg'] = [analyzer.polarity_scores(x)['neg'] for x in df['reviewText']]
df['neu'] = [analyzer.polarity_scores(x)['neu'] for x in df['reviewText']]
df['pos'] = [analyzer.polarity_scores(x)['pos'] for x in df['reviewText']]

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  This is separate from the ipykernel package so we can avoid doing imports until
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  after removing the cwd from sys.path.
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


#calling the dataframe
df


#Creating a dataframe of positive data
positive=df[df['compound'] > 0]


positive


#taking a sample of 1000 positive reviews
p=positive['reviewText'].iloc[1000]

p

'I\'ve read some of the other reviews, and I am surprised at the venom in some of the negative ones (who won\'t indicate they actually purchased the darn thing), especially as this book (a) cost a whopping dollar, and (b) this is a self-published author vs. from a large publishing house.Now that I have that off of my chest...I\'m not going to have a spoiler as another reviewer did but I will say I enjoyed this book, and it reminded me a little of "Alas Babylon" but with a new science fiction twist.  As someone who spent many summers at camp, I could put myself into some of the character\'s shoes and relate.  If you\'re looking for a good story, and something that to me is worth good value for the dollar sales price, this will be worth your time with an interesting story and characters.  The characters could have more development, and the author kind of left it open in the end with the possibility of a sequel which I would like to read if it is written.  I did take away one star for a few grammatical and formatting errors, but it by no means takes away from the overall enjoyment.  Nice first book from an independent author.'


pip install spacy && python -m spacy download en

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2022-09-28 02:00:24.792351: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
⚠ As of spaCy v3.0, shortcuts like 'en' are deprecated. Please use the
full pipeline package name 'en_core_web_sm' instead.
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting en-core-web-sm==3.4.0
  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.0/en_core_web_sm-3.4.0-py3-none-any.whl (12.8 MB)
     |████████████████████████████████| 12.8 MB 32.8 MB/s 
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✔ Download and installation successful
You can now load the package via spacy.load('en_core_web_sm')


import string 
import spacy 


nlp = spacy.load("en_core_web_sm")


#defining a function for lemmatisation 
def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'ADV']):
    """https://spacy.io/api/annotation"""
    texts_out = []
    for sent in texts:
        doc = nlp(" ".join(sent)) 
        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])
    return texts_out


#defining a finction to remove punctuation
def remove_punctuation(text):
    punctuationfree="".join([i for i in text if i not in string.punctuation])
    return punctuationfree

p

'I\'ve read some of the other reviews, and I am surprised at the venom in some of the negative ones (who won\'t indicate they actually purchased the darn thing), especially as this book (a) cost a whopping dollar, and (b) this is a self-published author vs. from a large publishing house.Now that I have that off of my chest...I\'m not going to have a spoiler as another reviewer did but I will say I enjoyed this book, and it reminded me a little of "Alas Babylon" but with a new science fiction twist.  As someone who spent many summers at camp, I could put myself into some of the character\'s shoes and relate.  If you\'re looking for a good story, and something that to me is worth good value for the dollar sales price, this will be worth your time with an interesting story and characters.  The characters could have more development, and the author kind of left it open in the end with the possibility of a sequel which I would like to read if it is written.  I did take away one star for a few grammatical and formatting errors, but it by no means takes away from the overall enjoyment.  Nice first book from an independent author.'


import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
stop_words = stopwords.words('english')
stop_words.extend(['crypto','even','early'])
# Define functions for stopwords, bigrams, trigrams and lemmatization
def remove_stopwords(texts):
    return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]
def make_bigrams(texts):
    return [bigram_mod[doc] for doc in texts]
def make_trigrams(texts):
    return [trigram_mod[bigram_mod[doc]] for doc in texts]
def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'ADV']):
    """https://spacy.io/api/annotation"""
    texts_out = []
    for sent in texts:
        doc = nlp(" ".join(sent)) 
        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])
    return texts_out

[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Unzipping corpora/stopwords.zip.


# Build the bigram and trigram models
bigram = gensim.models.Phrases(positive.reviewText, min_count=50, threshold=100) # higher threshold fewer phrases.
trigram = gensim.models.Phrases(bigram[positive.reviewText], threshold=100)
# Faster way to get a sentence clubbed as a trigram/bigram
bigram_mod = gensim.models.phrases.Phraser(bigram)
trigram_mod = gensim.models.phrases.Phraser(trigram)

/usr/local/lib/python3.7/dist-packages/gensim/models/phrases.py:598: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class
  warnings.warn("For a faster implementation, use the gensim.models.phrases.Phraser class")


# Remove Stop Words
data_words_nostops = remove_stopwords(positive.reviewText)
# Form Bigrams
data_words_bigrams = make_bigrams(data_words_nostops)
# Initialize spacy 'en' model, keeping only tagger component (for efficiency)
nlp = spacy.load("en_core_web_sm", disable=['parser', 'ner'])
data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ','ADV'])
print(data_lemmatized[:1])

[['mess', 'man', 'saloon', 'famous', 'bounty', 'hunter', 'saloon', 'year', 'long', 'quest', 'avenge', 'sister', 'murder', 'figure', 'next', 'snotty', 'nosed', 'farm', 'boy', 'gang', 'bully', 'money', 'man', 'ranch', 'reluctantly', 'man', 'justice', 'outright', 'first', 'need', 'sister', 'kyle', 'springer', 'bailey', 'trail', 'ground', 'month', 'jace', 'revenge', 'man', 'husband', 'ranch', 'crime', 'keen', 'detour', 'jace', 'option', 'boy', 'well', 'try', 'pace', 'confrontation', 'way', 'jace', 'discover', 'kyle', 'kyla', 'clean', 'whole', 'reason', 'scoundrel', 'dead', 'hope', 'still', 'book', 'share', 'touching', 'moment', 'slow', 'romance', 'kyla', 'good', 'reason', 'fear', 'man', 'boy', 'jace', 'slowly', 'shell', 'help', 'fear', 'pain', 'real', 'deeply', 'rooted', 'disappear', 'face', 'sexiness', 'understandable', 'aversion', 'marriage', 'magically', 'round', 'town', 'town', 'entire', 'adult', 'life', 'man', 'fair', 'share', 'man', 'way', 'good', 'husband', 'father', 'unique', 'position', 'help', 'time', 'however', 'favorite', 'theme', 'old', 'west', 'heroine', 'male', 'morally', 'ambiguous', 'hero', 'road', 'romance', 'kitchen', 'certain', 'distance', 'writing', 'character', 'distinct', 'fully', 'point', 'instance', 'bland', 'telling', 'never', 'really', 'quite', 'bit', 'exciting', 'trip', 'far', 'away', 'definitely', 'alongside', 'hasty', 'tidy', 'book', 'feeling', 'bit', 'less', 'completely', 'satisfied', 'book', 'still', 'book', 'author', 'note', 'ebook', 'version', 'lot', 'error', 'word', 'mentally', 'edit', 'read', 'sentence', 'sense', 'bad', 'little', 'distracting']]


data_lemmatized

[['jace',
  'rankin',
  'may',
  'short',
  'nothing',
  'mess',
  'man',
  'hauled',
  'saloon',
  'undertaker',
  'knows',
  'famous',
  'bounty',
  'hunter',
  'oregon',
  'shot',
  'man',
  'saloon',
  'finished',
  'years',
  'long',
  'quest',
  'avenge',
  'sister',
  'murder',
  'trying',
  'figure',
  'next',
  'snotty',
  'nosed',
  'farm',
  'boy',
  'rescued',
  'gang',
  'bullies',
  'offers',
  'money',
  'kill',
  'man',
  'forced',
  'ranch',
  'reluctantly',
  'agrees',
  'bring',
  'man',
  'justice',
  'kill',
  'outright',
  'first',
  'needs',
  'tell',
  'sister',
  'widower',
  'news',
  'kyla',
  'kyle',
  'springer',
  'bailey',
  'riding',
  'trails',
  'sleeping',
  'ground',
  'past',
  'month',
  'trying',
  'find',
  'jace',
  'wants',
  'revenge',
  'man',
  'killed',
  'husband',
  'took',
  'ranch',
  'amongst',
  'crimes',
  'keen',
  'detour',
  'jace',
  'wants',
  'take',
  'realizes',
  'options',
  'hides',
  'behind',
  'boy',
  'persona',
  'best',
  'tries',
  'keep',
  'pace',
  'confrontation',
  'along',
  'way',
  'gets',
  'shot',
  'jace',
  'discovers',
  'kyle',
  'kyla',
  'come',
  'clean',
  'whole',
  'reason',
  'needs',
  'scoundrel',
  'dead',
  'hope',
  'still',
  'help',
  'book',
  'share',
  'touching',
  'moments',
  'slow',
  'blooming',
  'romance',
  'kyla',
  'find',
  'good',
  'reason',
  'fear',
  'men',
  'hide',
  'behind',
  'boy',
  'persona',
  'watching',
  'jace',
  'slowly',
  'pull',
  'shell',
  'help',
  'conquer',
  'fears',
  'endearing',
  'pain',
  'real',
  'deeply',
  'rooted',
  'disappear',
  'face',
  'sexiness',
  'neither',
  'understandable',
  'aversion',
  'marriage',
  'magically',
  'disappear',
  'round',
  'nookie',
  'would',
  'man',
  'drifted',
  'town',
  'town',
  'entire',
  'adult',
  'life',
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  'killed',
  'fair',
  'share',
  'men',
  'along',
  'way',
  'feel',
  'make',
  'good',
  'husband',
  'father',
  'walking',
  'wounded',
  'unique',
  'position',
  'help',
  'need',
  'time',
  'realize',
  'however',
  'packed',
  'bursting',
  'favorite',
  'themes',
  'old',
  'west',
  'setting',
  'heroine',
  'passing',
  'male',
  'morally',
  'ambiguous',
  'hero',
  'wounded',
  'souls',
  'road',
  'romance',
  'kitchen',
  'sink',
  'certain',
  'distance',
  'writing',
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  'getting',
  'carried',
  'away',
  'characters',
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  'fully',
  'formed',
  'point',
  'glaring',
  'instances',
  'bland',
  'telling',
  'anything',
  'never',
  'felt',
  'really',
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  'exciting',
  'trip',
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  'far',
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  'run',
  'alongside',
  'add',
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  'ending',
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  'completely',
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  'enjoyed',
  'book',
  'say',
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  'read',
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  'book',
  'author',
  'note',
  'read',
  'released',
  'ebook',
  'version',
  'noticed',
  'lot',
  'formatting',
  'errors',
  'missing',
  'words',
  'mentally',
  'edit',
  'read',
  'sentences',
  'make',
  'sense',
  'bad',
  'little',
  'distracting'],
 ['great',
  'short',
  'read',
  'want',
  'put',
  'read',
  'one',
  'sitting',
  'sex',
  'scenes',
  'great',
  'two',
  'males',
  'one',
  'female',
  'character',
  'bit',
  'surprising',
  'never',
  'thought',
  'could',
  'learned',
  'something',
  'new',
  'really',
  'enjoyed',
  'reading',
  'book',
  'great',
  'way',
  'get',
  'hot',
  'bothered',
  'take',
  'advantage',
  'significant'],
 ['start',
  'saying',
  'first',
  'four',
  'books',
  'expecting',
  'conclude',
  'centers',
  'children',
  'alex',
  'younger',
  'brother',
  'argus',
  'alex',
  'friend',
  'sapphire',
  'find',
  'name',
  'bus',
  'map',
  'find',
  'mystery',
  'starts',
  'well',
  'written',
  'good',
  'character',
  'development',
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  'lots',
  'room',
  'still',
  'quite',
  'short',
  'book',
  'moving',
  'onto',
  'book',
  'swiftly'],
 ['expect', 'type', 'book', 'library', 'pleased', 'find', 'price', 'right'],
 ['aislinn',
  'little',
  'girl',
  'big',
  'dreams',
  'death',
  'older',
  'brother',
  'decides',
  'follow',
  'footsteps',
  'become',
  'lady',
  'knight',
  'quest',
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  'brings',
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  'forbidden',
  'love',
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  'knight',
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  'kingdom',
  'front',
  'personal',
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  'help',
  'journey',
  'fraught',
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  'filled',
  'adventure',
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  'forced',
  'grow',
  'fast',
  'must',
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  'develop',
  'skills',
  'necessary',
  'realize',
  'lifelong',
  'dream',
  'sword',
  'fights',
  'passionate',
  'love',
  'mythical',
  'creatures',
  'courtly',
  'games',
  'woman',
  'honor',
  'kind',
  'book',
  'love',
  'idea',
  'incorporating',
  'real',
  'history',
  'fantasy',
  'ms',
  'zoltack',
  'book',
  'based',
  'real',
  'life',
  'story',
  'group',
  'female',
  'knights',
  'blending',
  'history',
  'fiction',
  'writing',
  'full',
  'plot',
  'adventure',
  'characters',
  'strong',
  'believable',
  'grew',
  'love',
  'cared',
  'hardships',
  'successes',
  'aislinn',
  'quite',
  'main',
  'protagonist',
  'tenacious',
  'bold',
  'beautiful',
  'strong',
  'romance',
  'blooms',
  'prince',
  'well',
  'developed',
  'years',
  'solid',
  'friendship',
  'teasing',
  'enjoyed',
  'reading',
  'secret',
  'courtship',
  'idea',
  'romance',
  'would',
  'turn',
  'ms',
  'zoltack',
  'twists',
  'store',
  'shock',
  'startle',
  'well',
  'please',
  'conclusion',
  'thoroughly',
  'recommend',
  'book',
  'everyone',
  'enjoys',
  'fantasy',
  'romance',
  'adventure',
  'eagerly',
  'awaiting',
  'sequel',
  'knight',
  'glory'],
 ['got',
  'like',
  'collaborated',
  'short',
  'stories',
  'alot',
  'times',
  'two',
  'good',
  'writers',
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  'results',
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  'lesson',
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  'picking',
  'hitchhiker',
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  'hitchhiking',
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  'accounts',
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  'video',
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  'break',
  'short',
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  'idea',
  'cool',
  'one',
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  'without',
  'talking',
  'tier',
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  'pieces',
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  'pretty',
  'graphic',
  'details',
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  'mind',
  'little',
  'detail',
  'short',
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  'pick',
  'one'],
 ['loved',
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  'hooked',
  'series',
  'hope',
  'kelsey',
  'maxwell',
  'really',
  'made',
  'characters',
  'get',
  'enough'],
 ['good',
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  'short',
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  'tease',
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  'everything',
  'quick',
  'kick',
  'kick',
  'comes',
  'dark'],
 ['though',
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  'outcome',
  'still',
  'enjoyable',
  'erotic',
  'read',
  'little',
  'short',
  'taste'],
 ['finished',
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  'wicked',
  'garden',
  'say',
  'wow',
  'novella',
  'lorelei',
  'james',
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  'hot',
  'reader',
  'starts',
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  'wicked',
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  'extra',
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  'sumthin',
  'billy',
  'buchanan',
  'returns',
  'spearfish',
  'favor',
  'business',
  'associate',
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  'favor',
  'also',
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  'give',
  'chance',
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  'spark',
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  'school',
  'flame',
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  'lacroix',
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  'years',
  'regretted',
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  'eden',
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  'years',
  'building',
  'career',
  'dedicated',
  'local',
  'community',
  'center',
  'helping',
  'risk',
  'kids',
  'time',
  'real',
  'relationships',
  'occasional',
  'strings',
  'attached',
  'hook',
  'ups',
  'sexy',
  'native',
  'american',
  'rock',
  'star',
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  'white',
  'feather',
  'billy',
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  'time',
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  'chemistry',
  'spikes',
  'charts',
  'eden',
  'determined',
  'fight',
  'billy',
  'working',
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  'job',
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  'project',
  'things',
  'heat',
  'eden',
  'billy',
  'realizing',
  'still',
  'strong',
  'feelings',
  'dunh',
  'dunh',
  'dunh',
  'jon',
  'comes',
  'home',
  'couple',
  'days',
  'billy',
  'jon',
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  'one',
  'darkest',
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  'boy',
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  'really',
  'like',
  'eden',
  'though',
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  'heart',
  'billy',
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  'charge',
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  'billy',
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  'dominant',
  'vulnerable',
  'absolutely',
  'adore',
  'way',
  'treats',
  'eden',
  'insecurities',
  'displays',
  'jon',
  'oh',
  'favorite',
  'get',
  'wrong',
  'love',
  'billy',
  'way',
  'jon',
  'treats',
  'eden',
  'menage',
  'melts',
  'heart',
  'among',
  'things',
  'mention',
  'jon',
  'sexy',
  'rock',
  'star',
  'moon',
  'excited',
  'jon',
  'getting',
  'novella',
  'ballroom',
  'blitz',
  'release',
  'date',
  'folks',
  'let',
  'tell',
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  'get',
  'action',
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  'work',
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  'wicked',
  'garden',
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  'room',
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  'ten',
  'times',
  'day',
  'wicked',
  'garden',
  'great',
  'novella',
  'perfect',
  'afternoon',
  'want',
  'escape',
  'hours',
  'bonus',
  'get',
  'see',
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  'lorelei',
  'writes',
  'non',
  'cowboys',
  'yep',
  'cowboy',
  'hat',
  'suit',
  'ms',
  'james',
  'knows',
  'write',
  'sexy',
  'hell',
  'heroes'],
 ['king',
  'james',
  'version',
  'far',
  'tell',
  'may',
  'know',
  'kjv',
  'published',
  'books',
  'included',
  'get',
  'ebook',
  'kjv',
  'apocrypha',
  'kindle',
  'bible',
  'kjv',
  'apocrypha',
  'best',
  'navigation',
  'direct',
  'verse',
  'jump',
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  'james',
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  'want',
  'read',
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  'provided',
  'short',
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  'either',
  'halley',
  'bible',
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  'bible',
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  'new',
  'international',
  'version',
  'deluxe',
  'editionor',
  'new',
  'unger',
  'bible',
  'handbookthe',
  'new',
  'unger',
  'bible',
  'handbook',
  'available',
  'kindle',
  'ebooks'],
 ['disappointed',
  'book',
  'anything',
  'blonds',
  'one',
  'figured',
  'jokes',
  'good',
  'laughs',
  'wrong',
  'first',
  'count',
  'real',
  'surprised',
  'jokes',
  'book',
  'second',
  'among',
  'possible',
  'jokes',
  'least',
  'repeated',
  'time',
  'different',
  'parts',
  'book',
  'almost',
  'writer',
  'kept',
  'running',
  'list',
  'jokes',
  'heard',
  'real',
  'concern',
  'whether',
  'might',
  'already',
  'recorded',
  'decided',
  'must',
  'least',
  'jokes',
  'finally',
  'perfectly',
  'frank',
  'recall',
  'laughing',
  'single',
  'one',
  'jokes',
  'better',
  'described',
  'insults',
  'mistaking',
  'degradation',
  'group',
  'people',
  'humor',
  'th',
  'grade',
  'material',
  'bother',
  'one',
  'worth',
  'cents',
  'worth',
  'free'],
 ['super',
  'quick',
  'book',
  'wish',
  'would',
  'longer',
  'really',
  'liked',
  'characters',
  'sexy',
  'bedroom',
  'pool',
  'table',
  'sex',
  'good',
  'author'],
 ['love',
  'wards',
  'books',
  'story',
  'completely',
  'awsome',
  'zsadist',
  'favorite',
  'series'],
 ['first',
  'author',
  'thoroughly',
  'enjoyed',
  'say',
  'concept',
  'dragon',
  'elves',
  'djinn',
  'war',
  'new',
  'theres',
  'usually',
  'truce',
  'place',
  'elves',
  'djinn',
  'team',
  'eliminate',
  'dragon',
  'mates',
  'attempt',
  'wipe',
  'dragon',
  'race',
  'refreshing',
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  'mean',
  'scarlet',
  'pop',
  'star',
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  'years',
  'janos',
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  'also',
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  'mate',
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  'must',
  'protect',
  'till',
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  'accomplished',
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  'thus',
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  'way',
  'protect',
  'story',
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  'pages',
  'still',
  'lot',
  'told',
  'short',
  'space',
  'looking',
  'forward',
  'continuing',
  'saga',
  'next',
  'book'],
 ['first',
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  'bast',
  'book',
  'really',
  'liked',
  'looked',
  'reads',
  'books',
  'seems',
  'little',
  'far',
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  'bunnies',
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  'fact',
  'quite',
  'ways',
  'book',
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  'one',
  'would',
  'highly',
  'recommend',
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  'machine',
  'brought',
  'today',
  'love',
  'interest',
  'elisse',
  'trying',
  'escape',
  'clutches',
  'would',
  'preferred',
  'music',
  'portrayal',
  'mozart',
  'quite',
  'fun',
  'evident',
  'author',
  'based',
  'tom',
  'hulce',
  'performance',
  'movie',
  'amadeus',
  'note',
  'book',
  'movie',
  'mentioned',
  'several',
  'times',
  'connection',
  'accident',
  'must',
  'mention',
  'one',
  'fantastic',
  'scene',
  'mozart',
  'gets',
  'fiddle',
  'contest',
  'rainman',
  'honky',
  'tonk',
  'bar',
  'new',
  'orleans',
  'mozart',
  'plays',
  'stradivarius',
  'real',
  'hoot',
  'time',
  'travel',
  'aspects',
  'back',
  'story',
  'evil',
  'company',
  'device',
  'manipulations',
  'never',
  'revealed',
  'much',
  'ok',
  'emphasis',
  'mozart',
  'escape',
  'imprisonment',
  'short',
  'liked',
  'book',
  'premise',
  'much',
  'book',
  'much',
  'long',
  'major',
  'trimming',
  'would',
  'helped',
  'enormously',
  'downgraded',
  'one',
  'star',
  'length',
  'final',
  'word',
  'kindle',
  'formatting',
  'find',
  'troubling',
  'previous',
  'reviewers',
  'errors',
  'many'],
 ['basic',
  'storyline',
  'good',
  'writing',
  'style',
  'stilted',
  'phrasing',
  'often',
  'awkward',
  'lacks',
  'good',
  'flow',
  'townspeople',
  'era',
  'phobia',
  'native',
  'american',
  'quick',
  'accept',
  'native',
  'lady',
  'know',
  'speak',
  'english',
  'realistic',
  'finish',
  'book'],
 ['fantastic',
  'epilogue',
  'book',
  'dirty',
  'highly',
  'recommend',
  'read',
  'book',
  'read',
  'novella',
  'follow',
  'dan',
  'elle',
  'relationship',
  'boy',
  'hot'],
 ['loved',
  'first',
  'one',
  'series',
  'thought',
  'great',
  'start',
  'reading',
  'dissappointed',
  'read',
  'story',
  'line',
  'ending',
  'guy',
  'meets',
  'girl',
  'girl',
  'sleeps',
  'guy',
  'saves',
  'evil',
  'sleep',
  'together',
  'live',
  'happily',
  'every',
  'great',
  'sex',
  'scenes',
  'though'],
 ['great',
  'end',
  'triology',
  'love',
  'bought',
  'lovers',
  'situatation',
  'action',
  'romance',
  'drama',
  'love',
  'triangle',
  'must',
  'read',
  'next',
  'book',
  'hooks',
  'lovers'],
 ['read',
  'book',
  'minutes',
  'could',
  'figure',
  'written',
  'would',
  'write',
  'story',
  'like',
  'point',
  'great',
  'possibilities',
  'substance',
  'like',
  'reading',
  'diary',
  'end',
  'would',
  'recommend',
  'one',
  'anyone'],
 ['story',
  'promise',
  'fell',
  'short',
  'think',
  'problem',
  'story',
  'quite',
  'decide',
  'genre',
  'wanted',
  'bit',
  'succeeded',
  'none',
  'sort',
  'dystopia',
  'fantasy',
  'needed',
  'world',
  'building',
  'veered',
  'unevenly',
  'people',
  'using',
  'offworld',
  'technology',
  'security',
  'cameras',
  'clinging',
  'old',
  'ways',
  'riding',
  'horses',
  'fighting',
  'swords',
  'traditional',
  'romance',
  'little',
  'erotic',
  'gritty',
  'realistic',
  'kind',
  'deviant',
  'behavior',
  'desperate',
  'people',
  'would',
  'get',
  'endured',
  'hero',
  'heroine',
  'probably',
  'succeeded',
  'erotic',
  'story',
  'strictly',
  'speaking',
  'interaction',
  'hero',
  'heroine',
  'everything',
  'else',
  'glossed',
  'probably',
  'descriptive',
  'enough',
  'genre'],
 ['really',
  'liked',
  'story',
  'enjoyed',
  'brotherhood',
  'blood',
  'books',
  'like',
  'author',
  'tied',
  'series',
  'one',
  'another',
  'yet',
  'read',
  'series',
  'stands',
  'alone',
  'look',
  'forward'],
 ['good',
  'tittle',
  'much',
  'else',
  'bought',
  'got',
  'good',
  'title',
  'thanks'],
 ['generally',
  'reluctant',
  'take',
  'chance',
  'unknown',
  'unrecommended',
  'author',
  'good',
  'reviews',
  'price',
  'prompted',
  'give',
  'one',
  'shot',
  'really',
  'glad',
  'cassandra',
  'thinks',
  'knows',
  'exactly',
  'looking',
  'man',
  'tall',
  'dark',
  'handsome',
  'side',
  'good',
  'bed',
  'busy',
  'trying',
  'secure',
  'almost',
  'completely',
  'dismisses',
  'heat',
  'coming',
  'another',
  'source',
  'lavey',
  'humor',
  'really',
  'shines',
  'one',
  'heroine',
  'stuck',
  'clouds',
  'hero',
  'running',
  'attraction',
  'nice',
  'complaint',
  'would',
  'cassandra',
  'lost',
  'clouds',
  'moments',
  'go',
  'little',
  'long',
  'taste',
  'honestly',
  'lavey',
  'undercharging',
  'adventure',
  'though',
  'short',
  'well',
  'written',
  'well',
  'edited',
  'highly',
  'recommend',
  'look',
  'forward',
  'seeing',
  'future'],
 ['book',
  'bag',
  'took',
  'much',
  'effort',
  'get',
  'like',
  'involvement',
  'interest',
  'curiosity',
  'teased',
  'right',
  'beginning'],
 ['battle',
  'scottish',
  'clans',
  'mysterious',
  'warrior',
  'hunter',
  'saves',
  'life',
  'unconscious',
  'lachlan',
  'rouge',
  'fox',
  'macgowan',
  'lachlan',
  'brothers',
  'tease',
  'tenderly',
  'hunter',
  'carried',
  'battlefield',
  'safety',
  'evermyst',
  'lovers',
  'though',
  'resents',
  'owing',
  'anyone',
  'anything',
  'lachlan',
  'vowing',
  'pay',
  'debt',
  'wants',
  'thank',
  'warrior',
  'probably',
  'saved',
  'life',
  'lachlan',
  'still',
  'owes',
  'hunter',
  'rejects',
  'everything',
  'rogue',
  'fox',
  'offers',
  'lachlan',
  'become',
  'intrigued',
  'warrior',
  'swears',
  'learn',
  'secret',
  'great',
  'fighter',
  'uncover',
  'secret',
  'shocked',
  'beyond',
  'belief',
  'beginning',
  'understand',
  'fascination',
  'hunter',
  'observes',
  'warrior',
  'talking',
  'enemy',
  'evil',
  'stalks',
  'evermyst',
  'warrior',
  'bride',
  'may',
  'strongest',
  'lois',
  'greiman',
  'scottish',
  'romances',
  'cast',
  'makes',
  'quite',
  'exciting',
  'tale',
  'hunter',
  'wonderful',
  'warrior',
  'secrets',
  'surprise',
  'audience',
  'lachlan',
  'obsessive',
  'make',
  'remittance',
  'savior',
  'uncovers',
  'secret',
  'changes',
  'obsession',
  'secondary',
  'characters',
  'add',
  'depth',
  'tale',
  'provide',
  'much',
  'satisfaction',
  'readers',
  'sixteenth',
  'century',
  'romances',
  'harriet',
  'klausner'],
 ['picked',
  'paranormal',
  'romance',
  'quite',
  'time',
  'ago',
  'free',
  'formatting',
  'grammar',
  'bother',
  'enjoy',
  'pet',
  'peeve',
  'might',
  'able',
  'get',
  'beyond',
  'first',
  'chapter',
  'femilla',
  'goddess',
  'womankind',
  'duty',
  'right',
  'grievious',
  'wrongs',
  'committed',
  'women',
  'men',
  'keeping',
  'punished',
  'afterlife',
  'demon',
  'demeana',
  'brutalized',
  'women',
  'alive',
  'beyond',
  'redemption',
  'must',
  'prove',
  'rehabilitation',
  'andrew',
  'regent',
  'one',
  'men',
  'death',
  'tortured',
  'afterlife',
  'every',
  'one',
  'hundred',
  'years',
  'chance',
  'returns',
  'mortal',
  'life',
  'month',
  'find',
  'woman',
  'love',
  'loves',
  'return',
  'saved',
  'must',
  'withstand',
  'another',
  'hundred',
  'years',
  'torture',
  'try',
  'many',
  'make',
  'give',
  'consumed',
  'demon',
  'caitlin',
  'chance',
  'salvation',
  'get',
  'know',
  'one',
  'another',
  'caitlin',
  'starts',
  'dreaming',
  'life',
  'another',
  'woman',
  'catherine',
  'woman',
  'whose',
  'death',
  'caused',
  'andrew',
  'condemned',
  'afterlife',
  'typical',
  'time',
  'travel',
  'time',
  'slip',
  'romance',
  'fairly',
  'predictable',
  'excellent',
  'story',
  'bit',
  'trite',
  'times',
  'well',
  'constructed',
  'dialogue',
  'good',
  'well',
  'plotted',
  'consistent',
  'formatting',
  'errors',
  'crazy',
  'making',
  'much',
  'almost',
  'finish',
  'book',
  'grammar',
  'errors',
  'also',
  'really',
  'distract',
  'hero',
  'chaffed',
  'ankles',
  'heroine',
  'feeling',
  'better',
  'morning',
  'adulations',
  'oh',
  'andrew',
  'genus',
  'old',
  'weapons',
  'wonder',
  'species',
  'may',
  'seem',
  'minor',
  'glaring',
  'errors',
  'covers',
  'really',
  'bug',
  'instance',
  'story',
  'andrew',
  'supposed',
  'blonde',
  'cover',
  'model',
  'obviously',
  'yes',
  'yummy',
  'fit',
  'additionally',
  'never',
  'put',
  'bracers',
  'putting',
  'shirt',
  'unless',
  'plan',
  'fighting',
  'without',
  'one',
  'frankly',
  'happens',
  'conan',
  'movies',
  'frank',
  'frazetta',
  'artwork',
  'writing',
  'checked',
  'current',
  'publication',
  'date',
  'see',
  'maybe',
  'date',
  'download',
  'apparently',
  'case',
  'shows',
  'lack',
  'care',
  'professionalism',
  'several',
  'reviewers',
  'mentioned',
  'grammar',
  'formatting',
  'issues',
  'past',
  'quite',
  'shame',
  'book',
  'could',
  'star',
  'work',
  'bit',
  'effort'],
 ...]


import gensim.corpora as corpora
# Create Dictionary
id2word = corpora.Dictionary(data_lemmatized)
# Create Corpus
texts = data_lemmatized
# Term Document Frequency
corpus = [id2word.doc2bow(text) for text in texts]
# View
print(corpus[:1])

[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 2), (10, 1), (11, 4), (12, 1), (13, 3), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1), (19, 1), (20, 1), (21, 1), (22, 1), (23, 1), (24, 1), (25, 1), (26, 1), (27, 1), (28, 1), (29, 1), (30, 1), (31, 1), (32, 1), (33, 1), (34, 1), (35, 1), (36, 1), (37, 1), (38, 1), (39, 1), (40, 1), (41, 1), (42, 2), (43, 1), (44, 1), (45, 1), (46, 1), (47, 1), (48, 2), (49, 1), (50, 1), (51, 2), (52, 1), (53, 1), (54, 1), (55, 1), (56, 1), (57, 2), (58, 1), (59, 4), (60, 1), (61, 1), (62, 1), (63, 2), (64, 2), (65, 1), (66, 1), (67, 1), (68, 1), (69, 1), (70, 1), (71, 1), (72, 7), (73, 1), (74, 1), (75, 1), (76, 1), (77, 1), (78, 1), (79, 1), (80, 1), (81, 1), (82, 1), (83, 1), (84, 1), (85, 1), (86, 1), (87, 1), (88, 1), (89, 1), (90, 1), (91, 1), (92, 1), (93, 1), (94, 1), (95, 2), (96, 1), (97, 1), (98, 1), (99, 2), (100, 1), (101, 1), (102, 1), (103, 2), (104, 1), (105, 1), (106, 2), (107, 1), (108, 1), (109, 1), (110, 1), (111, 1), (112, 2), (113, 1), (114, 2), (115, 1), (116, 1), (117, 1), (118, 1), (119, 2), (120, 1), (121, 1), (122, 1), (123, 1), (124, 1), (125, 2), (126, 1), (127, 1), (128, 1), (129, 1), (130, 1), (131, 1), (132, 2), (133, 1), (134, 1), (135, 1), (136, 1), (137, 1), (138, 1)]]


# supporting function
def compute_coherence_values(corpus, dictionary, k, a, b):
    
    lda_model = gensim.models.LdaMulticore(corpus=corpus,
                                           id2word=dictionary,
                                           num_topics=k, 
                                           random_state=100,
                                           chunksize=100,
                                           passes=10,
                                           alpha=a,
                                           eta=b)
    
    coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, coherence='c_v')
    
    return coherence_model_lda.get_coherence()


lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
                                           id2word=id2word,
                                           num_topics= 10, #change it 1-10
                                           random_state=100,
                                           update_every=1,
                                           chunksize=10,
                                           passes=10,
                                           per_word_topics=True)


from gensim.models import CoherenceModel
# Compute Coherence Score for topic 2
coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, coherence='c_v')
coherence_lda = coherence_model_lda.get_coherence()
print('\nCoherence Score: ', coherence_lda)

Coherence Score:  0.29982964086572017


from pprint import pprint
# Print the Keyword in the 10 topics
pprint(lda_model.print_topics())
doc_lda = lda_model[corpus]

[(0,
  '0.048*"review" + 0.031*"kindle" + 0.027*"point" + 0.023*"sure" + '
  '0.022*"able" + 0.021*"happy" + 0.020*"maybe" + 0.018*"easy" + 0.016*"often" '
  '+ 0.016*"thought"'),
 (1,
  '0.046*"together" + 0.035*"pretty" + 0.034*"old" + 0.029*"family" + '
  '0.028*"idea" + 0.027*"year" + 0.027*"fast" + 0.020*"side" + '
  '0.016*"situation" + 0.014*"fan"'),
 (2,
  '0.025*"definitely" + 0.023*"mystery" + 0.019*"development" + 0.019*"writer" '
  '+ 0.018*"girl" + 0.018*"home" + 0.018*"love" + 0.016*"mind" + 0.015*"big" + '
  '0.015*"case"'),
 (3,
  '0.041*"reader" + 0.038*"job" + 0.036*"hard" + 0.025*"work" + 0.019*"light" '
  '+ 0.018*"soon" + 0.018*"wonderful" + 0.014*"future" + 0.012*"attention" + '
  '0.012*"attempt"'),
 (4,
  '0.046*"interesting" + 0.042*"friend" + 0.023*"heart" + 0.019*"man" + '
  '0.019*"twist" + 0.018*"truly" + 0.018*"feeling" + 0.016*"night" + '
  '0.012*"deep" + 0.012*"person"'),
 (5,
  '0.069*"series" + 0.030*"page" + 0.030*"star" + 0.030*"novel" + 0.027*"guy" '
  '+ 0.027*"part" + 0.024*"end" + 0.023*"people" + 0.021*"line" + '
  '0.020*"worth"'),
 (6,
  '0.050*"love" + 0.029*"life" + 0.026*"world" + 0.025*"woman" + '
  '0.020*"always" + 0.019*"day" + 0.017*"strong" + 0.016*"action" + '
  '0.016*"vampire" + 0.014*"full"'),
 (7,
  '0.114*"book" + 0.091*"story" + 0.042*"good" + 0.039*"character" + '
  '0.032*"well" + 0.029*"really" + 0.023*"author" + 0.019*"read" + '
  '0.019*"great" + 0.019*"time"'),
 (8,
  '0.054*"enough" + 0.036*"nice" + 0.023*"true" + 0.019*"probably" + '
  '0.015*"apocalyptic" + 0.013*"event" + 0.013*"professor" + 0.012*"animal" + '
  '0.011*"aspect" + 0.011*"tess"'),
 (9,
  '0.055*"man" + 0.022*"right" + 0.021*"erotic" + 0.020*"hot" + '
  '0.017*"relationship" + 0.017*"time" + 0.017*"quick" + 0.016*"year" + '
  '0.016*"last" + 0.014*"sweet"')]


import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS
comment_words = ''
stopwords = set(STOPWORDS)
# iterate through the csv file
for val in positive.reviewText:
     
    # typecaste each val to string
    val = str(val)
 
    # split the value
    tokens = val.split()
     
    # Converts each token into lowercase
    for i in range(len(tokens)):
        tokens[i] = tokens[i].lower()
     
    comment_words += " ".join(tokens)+" "
 
wordcloud = WordCloud(width = 800, height = 800,
                background_color ='white',
                stopwords = stopwords,
                min_font_size = 10).generate(comment_words)
 
# plot the WordCloud image                      
plt.figure(figsize = (8, 8), facecolor = None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad = 0)
 
plt.show()


#creating dataframe of negative data
negative=df[df['compound'] < 0]


negative


#Wordcloud for negative data
from wordcloud import WordCloud, STOPWORDS
comment_words = ''
stopwords = set(STOPWORDS)
# iterate through the csv file
for val in negative.reviewText:
     
    # typecaste each val to string
    val = str(val)
 
    # split the value
    tokens = val.split()
     
    # Converts each token into lowercase
    for i in range(len(tokens)):
        tokens[i] = tokens[i].lower()
     
    comment_words += " ".join(tokens)+" "
 
wordcloud = WordCloud(width = 800, height = 800,
                background_color ='white',
                stopwords = stopwords,
                min_font_size = 10).generate(comment_words)
 
# plot the WordCloud image                      
plt.figure(figsize = (8, 8), facecolor = None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad = 0)
 
plt.show()


def tokenize(document):
    tokens = [lemma(w) for w in document.split() if len(w) > 3 and w.isalpha()]
    return tokens


from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.stem import WordNetLemmatizer
lemma = WordNetLemmatizer().lemmatize
nltk.download('wordnet')
nltk.download('omw-1.4')

[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package omw-1.4 to /root/nltk_data...

True


vectorizer = TfidfVectorizer(tokenizer = tokenize, ngram_range = ((2,2)), stop_words = stopwords, strip_accents = 'unicode')


tdm = vectorizer.fit_transform(positive['reviewText'])


vectorizer.vocabulary_.items()

dict_items([('jace rankin', 106154), ('rankin nothing', 164086), ('nothing mess', 142888), ('mess hauled', 132574), ('hauled saloon', 92915), ('saloon undertaker', 178453), ('undertaker know', 221486), ('know famous', 111632), ('famous bounty', 70413), ('bounty hunter', 24661), ('hunter oregon', 99322), ('oregon shot', 146142), ('shot finished', 187521), ('finished year', 76000), ('year long', 238658), ('long quest', 121183), ('quest avenge', 162441), ('avenge murder', 13779), ('murder trying', 137568), ('trying figure', 219015), ('figure farm', 74057), ('farm rescued', 70857), ('rescued gang', 172985), ('gang bully', 82729), ('bully offer', 26989), ('offer money', 144650), ('money kill', 135156), ('kill forced', 109736), ('forced reluctantly', 78587), ('reluctantly agrees', 171969), ('agrees bring', 4058), ('bring kill', 25650), ('kill first', 109735), ('first need', 76584), ('need tell', 139877), ('tell widower', 208386), ('widower springer', 230873), ('springer bailey', 195219), ('bailey riding', 15379), ('riding trail', 175202), ('trail sleeping', 216931), ('sleeping ground', 190244), ('ground past', 89534), ('past month', 150175), ('month trying', 135432), ('trying find', 219017), ('find want', 75439), ('want revenge', 226830), ('revenge killed', 174185), ('killed husband', 109814), ('husband took', 99664), ('took amongst', 215631), ('amongst keen', 7179), ('keen detour', 108458), ('detour jace', 50719), ('jace want', 106157), ('want realizes', 226806), ('realizes hide', 168234), ('hide behind', 95611), ('behind persona', 18039), ('persona best', 152000), ('best try', 19310), ('try keep', 218903), ('keep confrontation', 108537), ('confrontation along', 39901), ('along shot', 5558), ('shot jace', 187526), ('jace discovers', 106151), ('discovers come', 53103), ('come clean', 37288), ('clean reason', 35582), ('reason need', 169400), ('need scoundrel', 139817), ('scoundrel dead', 180542), ('dead hope', 46485), ('hope still', 97655), ('still help', 197914), ('help book', 94295), ('book share', 23482), ('share touching', 186154), ('touching moment', 216359), ('moment find', 134966), ('find good', 74967), ('good reason', 87263), ('reason fear', 169345), ('fear hide', 71717), ('behind watching', 18081), ('watching jace', 228063), ('jace slowly', 106156), ('slowly pull', 190609), ('pull shell', 161478), ('shell help', 186401), ('help conquer', 94326), ('conquer fear', 40303), ('fear pain', 71738), ('pain real', 148260), ('real disappear', 167526), ('disappear face', 52578), ('face neither', 68915), ('neither understandable', 140324), ('understandable aversion', 221365), ('aversion marriage', 13825), ('marriage magically', 129305), ('magically disappear', 126323), ('disappear round', 52583), ('round drifted', 177204), ('drifted town', 56098), ('town town', 216724), ('town entire', 216621), ('entire adult', 62618), ('adult life', 3188), ('life killed', 117218), ('killed fair', 109805), ('fair share', 69554), ('share along', 186034), ('along 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country', 233694), ('country surely', 42644), ('surely found', 204350), ('found perfectly', 79852), ('remember long', 172157), ('long think', 121244), ('took enough', 215674), ('enough advantage', 61849), ('advantage plot', 3315), ('plot afraid', 154682), ('afraid encorporate', 3678), ('encorporate fully', 59623), ('glad part', 85269), ('kind dragged', 110025), ('dragged glad', 55369), ('stuck nice', 201938), ('nice adam', 141341), ('adam changing', 2386), ('changing way', 31561), ('way throughout', 228247), ('throughout knew', 212965), ('knew really', 111224), ('tell started', 208330), ('realizing though', 168319), ('never say', 140814), ('enjoyed onto', 61497), ('onto rest', 145455), ('love fine', 123323), ('fine leather', 75664), ('leather case', 115539), ('case available', 29624), ('many beautiful', 128356), ('beautiful justify', 16643), ('justify buying', 108035), ('buying gift', 27468), ('gift needed', 83972), ('case protect', 29727), ('protect kindle', 160604), ('bill almost', 20200), ('almost soft', 5239), ('soft sturdy', 191467), ('sturdy good', 202242), ('smooth zipper', 191062), ('zipper last', 239441), ('long complaint', 121004), ('complaint black', 38714), ('black blend', 20527), ('blend everything', 20845), ('everything hard', 65845), ('hard spot', 92394), ('spot sometimes', 195147), ('sometimes desk', 192635), ('desk many', 49950), ('black computer', 20533), ('computer going', 39297), ('make stand', 127421), ('stand wish', 195619), ('wish color', 232450), ('color option', 37034), ('option knew', 145909), ('knew black', 111093), ('black going', 20551), ('cause price', 30316), ('excellent insight', 66447), ('insight early', 102818), ('tank combined', 207296), ('combined arm', 37186), ('arm armor', 10550), ('armor requires', 10589), ('requires infantry', 172870), ('infantry order', 102094), ('order successfully', 146061), ('successfully exploit', 202850), ('exploit last', 68152), ('deal effect', 46604), ('effect modern', 58246), ('military training', 133411), ('training average', 217000), ('average draftee', 13788), ('draftee unique', 55333), ('unique considering', 221916), ('author suggesting', 13542), ('suggesting technique', 203226), ('valentine cupid', 223737), ('cupid jena', 44833), ('jena another', 106827), ('four friend', 80135), ('vampire mate', 223994), ('mate half', 129963), ('half vampire', 90781), ('vampire another', 223851), ('read seven', 165856), ('enjoyed optimism', 61498), ('optimism coming', 145893), ('coming martial', 37954), ('art scene', 11141), ('looking foreward', 121923), ('foreward book', 78723), ('added found', 2546), ('found side', 79949), ('defined enjoy', 47833), ('action apocalyptic', 1667), ('apocalyptic series', 9553), ('really situation', 169034), ('situation capturing', 189457), ('capturing woman', 28865), ('woman workplace', 234155), ('workplace tying', 235736), ('tying bunch', 220168), ('bunch guy', 27034), ('around turn', 10944), ('turn somehow', 219501), ('somehow crossed', 191773), ('line memorable', 118792), ('memorable know', 132002), ('know wish', 112137), ('read joey', 165439), ('joey vampire', 107218), ('instruction look', 103354), ('look often', 121649), ('often hidden', 144919), ('hidden world', 95606), ('world sexual', 236068), ('sexual bdsm', 185282), ('telling sense', 208476), ('really characterization', 168446), ('characterization seemed', 33089), ('seemed overwhelmed', 182316), ('overwhelmed explicit', 147260), ('scene composed', 179681), ('composed wonder', 39253), ('wonder craig', 234195), ('craig money', 43615), ('money dungeon', 135127), ('dungeon built', 56649), ('built much', 26929), ('much traveling', 137338), ('traveling salary', 217362), ('salary know', 178416), ('know personal', 111900), ('experience poorly', 67710), ('poorly academic', 156041), ('academic find', 824)])


tfidf_weights = [(word, tdm.getcol(idx).sum()) for word, idx in vectorizer.vocabulary_.items()]


tfidf_weights[0:10]

[('jace rankin', 0.1939487587305963),
 ('rankin nothing', 0.07554804094199691),
 ('nothing mess', 0.07554804094199691),
 ('mess hauled', 0.07554804094199691),
 ('hauled saloon', 0.07554804094199691),
 ('saloon undertaker', 0.07554804094199691),
 ('undertaker know', 0.07554804094199691),
 ('know famous', 0.07554804094199691),
 ('famous bounty', 0.07554804094199691),
 ('bounty hunter', 0.9877735707246484)]


#Creating wordcloud for positive TFIDF Data
w = WordCloud(width=1500, height=1200, mode='RGBA', background_color='white', max_words=2000).fit_words(dict(tfidf_weights))


plt.figure(figsize=(20,15))
plt.imshow(w)
plt.axis('off')
plt.savefig('recipes_wordcloud.png')

	Unnamed: 0	Unnamed: 0.1	asin	helpful	rating	reviewText	reviewTime	reviewerID	reviewerName	summary	unixReviewTime
0	0	11539	B0033UV8HI	[8, 10]	3	Jace Rankin may be short, but he's nothing to ...	09 2, 2010	A3HHXRELK8BHQG	Ridley	Entertaining But Average	1283385600
1	1	5957	B002HJV4DE	[1, 1]	5	Great short read. I didn't want to put it dow...	10 8, 2013	A2RGNZ0TRF578I	Holly Butler	Terrific menage scenes!	1381190400
2	2	9146	B002ZG96I4	[0, 0]	3	I'll start by saying this is the first of four...	04 11, 2014	A3S0H2HV6U1I7F	Merissa	Snapdragon Alley	1397174400
3	3	7038	B002QHWOEU	[1, 3]	3	Aggie is Angela Lansbury who carries pocketboo...	07 5, 2014	AC4OQW3GZ919J	Cleargrace	very light murder cozy	1404518400
4	4	1776	B001A06VJ8	[0, 1]	4	I did not expect this type of book to be in li...	12 31, 2012	A3C9V987IQHOQD	Rjostler	Book	1356912000
...	...	...	...	...	...	...	...	...	...	...	...
11995	11995	2183	B001DUGORO	[0, 0]	4	Valentine cupid is a vampire- Jena and Ian ano...	02 28, 2014	A1OKS5Q1HD8WQC	lisa jon jung	jena	1393545600
11996	11996	6272	B002JCSFSQ	[2, 2]	5	I have read all seven books in this series. Ap...	05 16, 2011	AQRSPXLNEQAMA	TerryLP	Peacekeepers Series	1305504000
11997	11997	12483	B0035N1V7K	[0, 1]	3	This book really just wasn't my cuppa. The si...	07 26, 2013	A2T5QLT5VXOJAK	hwilson	a little creepy	1374796800
11998	11998	3640	B001W1XT40	[1, 2]	1	tried to use it to charge my kindle, it didn't...	09 17, 2013	A28MHD2DDY6DXB	Allison A. Slater "Gryphon50"	didn't work	1379376000
11999	11999	11398	B003370JUS	[5, 6]	3	Taking Instruction is a look into the often hi...	07 5, 2012	A3JUXLB4K9ZXCC	Dafna Yee	If you like BDSM with a touch of romance, this...	1341446400

	reviewText	summary	compound	neg	neu	pos
0	Jace Rankin may be short, but he's nothing to ...	Entertaining But Average	0.9559	0.129	0.708	0.163
1	Great short read. I didn't want to put it dow...	Terrific menage scenes!	0.9645	0.044	0.673	0.283
2	I'll start by saying this is the first of four...	Snapdragon Alley	0.8555	0.000	0.862	0.138
3	Aggie is Angela Lansbury who carries pocketboo...	very light murder cozy	-0.1280	0.078	0.851	0.070
4	I did not expect this type of book to be in li...	Book	0.4404	0.000	0.861	0.139
...	...	...	...	...	...	...
11995	Valentine cupid is a vampire- Jena and Ian ano...	jena	0.8172	0.000	0.762	0.238
11996	I have read all seven books in this series. Ap...	Peacekeepers Series	0.9685	0.082	0.658	0.261
11997	This book really just wasn't my cuppa. The si...	a little creepy	0.0644	0.022	0.952	0.026
11998	tried to use it to charge my kindle, it didn't...	didn't work	-0.7089	0.191	0.809	0.000
11999	Taking Instruction is a look into the often hi...	If you like BDSM with a touch of romance, this...	0.7650	0.000	0.914	0.086

	reviewText	summary	compound	neg	neu	pos
0	Jace Rankin may be short, but he's nothing to ...	Entertaining But Average	0.9559	0.129	0.708	0.163
1	Great short read. I didn't want to put it dow...	Terrific menage scenes!	0.9645	0.044	0.673	0.283
2	I'll start by saying this is the first of four...	Snapdragon Alley	0.8555	0.000	0.862	0.138
4	I did not expect this type of book to be in li...	Book	0.4404	0.000	0.861	0.139
5	Aislinn is a little girl with big dreams. Afte...	A story of a little girl with big dreams.	0.9963	0.080	0.643	0.277
...	...	...	...	...	...	...
11994	Excellent insight into early tank use, you can...	Pretty good	0.7579	0.022	0.867	0.111
11995	Valentine cupid is a vampire- Jena and Ian ano...	jena	0.8172	0.000	0.762	0.238
11996	I have read all seven books in this series. Ap...	Peacekeepers Series	0.9685	0.082	0.658	0.261
11997	This book really just wasn't my cuppa. The si...	a little creepy	0.0644	0.022	0.952	0.026
11999	Taking Instruction is a look into the often hi...	If you like BDSM with a touch of romance, this...	0.7650	0.000	0.914	0.086

	reviewText	summary	compound	neg	neu	pos
3	Aggie is Angela Lansbury who carries pocketboo...	very light murder cozy	-0.1280	0.078	0.851	0.070
6	This has the makings of a good story... unfort...	This story has potential but ultimately disapp...	-0.5106	0.086	0.880	0.034
10	I received a copy of book to review. "A secret...	Um, no.	-0.5719	0.099	0.901	0.000
12	I read the other reviews and decided to give i...	Truly Awful	-0.7391	0.129	0.871	0.000
17	How many times is author going to get Amazon t...	How in the world is this so highly rated?	-0.5171	0.079	0.862	0.059
...	...	...	...	...	...	...
11969	The plot for this book was well thought out, b...	First book?	-0.6008	0.077	0.901	0.022
11974	Such a shame that leaders controlling people e...	Church's hurt, hurt. People hurt people.	-0.2865	0.140	0.730	0.130
11982	This book started off good, but somewhere alon...	So much potentail	-0.9783	0.163	0.716	0.121
11983	If Terry Pratchett's Samuel Vimes and Dashiel ...	Unusual, gripping, a damn good read	-0.9200	0.159	0.703	0.138
11998	tried to use it to charge my kindle, it didn't...	didn't work	-0.7089	0.191	0.809	0.000